AP-OOD: Attention Pooling for Out-of-Distribution Detection

• URL: http://arxiv.org/abs/2602.06031v1
• Date: Thu, 05 Feb 2026 18:59:01 GMT
• Title: AP-OOD: Attention Pooling for Out-of-Distribution Detection
• Abstract summary: Out-of-distribution (OOD) detection is critical for the reliable deployment of machine learning models.<n>We propose AP-OOD, a novel OOD detection method for natural language.
• Score: 23.61044327325283
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Out-of-distribution (OOD) detection, which maps high-dimensional data into a scalar OOD score, is critical for the reliable deployment of machine learning models. A key challenge in recent research is how to effectively leverage and aggregate token embeddings from language models to obtain the OOD score. In this work, we propose AP-OOD, a novel OOD detection method for natural language that goes beyond simple average-based aggregation by exploiting token-level information. AP-OOD is a semi-supervised approach that flexibly interpolates between unsupervised and supervised settings, enabling the use of limited auxiliary outlier data. Empirically, AP-OOD sets a new state of the art in OOD detection for text: in the unsupervised setting, it reduces the FPR95 (false positive rate at 95% true positives) from 27.84% to 4.67% on XSUM summarization, and from 77.08% to 70.37% on WMT15 En-Fr translation.

Related papers

• ProOOD: Prototype-Guided Out-of-Distribution 3D Occupancy Prediction [77.70461178466043]
  3D semantic occupancy prediction is central to autonomous driving.<n>Current methods are vulnerable to long-tailed class bias and out-of-distribution (OOD) inputs.<n>We present ProOOD, a lightweight, plug-and-play method that couples prototype-guided refinement with training-free OOD scoring.
  arXiv  Detail & Related papers  (2026-04-01T16:11:59Z)
• Retrieval-Augmented Prompt for OOD Detection [23.092622473716144]
  Out-of-Distribution (OOD) detection is crucial for the reliable deployment of machine learning models in-the-wild.<n>We propose a novel OOD detection method called Retrieval-Augmented Prompt (RAP)<n>RAP augments a pre-trained vision-language model's prompts by retrieving external knowledge, offering enhanced semantic supervision for OOD detection.<n>RAP achieves state-of-the-art performance on large-scale OOD detection benchmarks.
  arXiv  Detail & Related papers  (2025-08-14T11:52:43Z)
• OODD: Test-time Out-of-Distribution Detection with Dynamic Dictionary [10.19006278808466]
  Out-of-distribution (OOD) detection remains challenging for deep learning models.<n>We propose OODD, a novel test-time OOD detection method that dynamically maintains and updates an OOD dictionary without fine-tuning.<n>We present an optimized variant of the KNN-based OOD detection framework that achieves a 3x speedup while maintaining detection performance.
  arXiv  Detail & Related papers  (2025-03-13T15:41:56Z)
• Can OOD Object Detectors Learn from Foundation Models? [56.03404530594071]
  Out-of-distribution (OOD) object detection is a challenging task due to the absence of open-set OOD data. Inspired by recent advancements in text-to-image generative models, we study the potential of generative models trained on large-scale open-set data to synthesize OOD samples. We introduce SyncOOD, a simple data curation method that capitalizes on the capabilities of large foundation models.
  arXiv  Detail & Related papers  (2024-09-08T17:28:22Z)
• Iterative Deployment Exposure for Unsupervised Out-of-Distribution Detection [5.019613806273252]
  Iterative Deployment Exposure (IDE) is a novel and more realistic setting for out-of-distribution (OOD) detection.<n> CSO uses a new U-OOD scoring function that combines the Mahalanobis distance with a nearest-neighbor approach.<n>We validate our approach on a dedicated benchmark, showing that our method greatly improves upon strong baselines on three medical imaging modalities.
  arXiv  Detail & Related papers  (2024-06-04T13:57:34Z)
• Model-free Test Time Adaptation for Out-Of-Distribution Detection [62.49795078366206]
  We propose a Non-Parametric Test Time textbfAdaptation framework for textbfDistribution textbfDetection (abbr) abbr utilizes online test samples for model adaptation during testing, enhancing adaptability to changing data distributions. We demonstrate the effectiveness of abbr through comprehensive experiments on multiple OOD detection benchmarks.
  arXiv  Detail & Related papers  (2023-11-28T02:00:47Z)
• Unsupervised Evaluation of Out-of-distribution Detection: A Data-centric Perspective [55.45202687256175]
  Out-of-distribution (OOD) detection methods assume that they have test ground truths, i.e., whether individual test samples are in-distribution (IND) or OOD. In this paper, we are the first to introduce the unsupervised evaluation problem in OOD detection. We propose three methods to compute Gscore as an unsupervised indicator of OOD detection performance.
  arXiv  Detail & Related papers  (2023-02-16T13:34:35Z)
• Rethinking Out-of-distribution (OOD) Detection: Masked Image Modeling is All You Need [52.88953913542445]
  We find surprisingly that simply using reconstruction-based methods could boost the performance of OOD detection significantly. We take Masked Image Modeling as a pretext task for our OOD detection framework (MOOD)
  arXiv  Detail & Related papers  (2023-02-06T08:24:41Z)
• ATOM: Robustifying Out-of-distribution Detection Using Outlier Mining [51.19164318924997]
  Adrial Training with informative Outlier Mining improves robustness of OOD detection. ATOM achieves state-of-the-art performance under a broad family of classic and adversarial OOD evaluation tasks.
  arXiv  Detail & Related papers  (2020-06-26T20:58:05Z)
• Robust Out-of-distribution Detection for Neural Networks [51.19164318924997]
  We show that existing detection mechanisms can be extremely brittle when evaluating on in-distribution and OOD inputs. We propose an effective algorithm called ALOE, which performs robust training by exposing the model to both adversarially crafted inlier and outlier examples.
  arXiv  Detail & Related papers  (2020-03-21T17:46:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.